Isolation and Characterization of Microsatellite Markers in Garcinia Gummi-Gutta by Next-Generation Sequencing and Cross-Species Ampliﬁcation

c Indian Academy of Sciences

RESEARCH ARTICLE

Isolation and characterization of microsatellite markers in Garcinia gummi-gutta by next-generation sequencing and cross-species ampliﬁcation

K. V. RAVISHANKAR1∗, R. VASUDEVA2, BYATROY HEMANTH1, B. S. SANDYA1,B.R.STHAPIT3, V. A. PARTHASARATHY1 and V. RAMANATHA RAO4

1ICAR-Indian Institute of Horticultural Research, Bengaluru 560 089, India 2Department of Forest Biology and Tree Improvement, College of Forestry Sirsi, University of Agricultural Sciences, Dharwad 581 401, India 3Biodiversity International, 93.4, Dharhara Marg, Fulbari, Ward no.11, Pokhara, Nepal 4Bioversity International, University of Horticultural Sciences, GKVK, Bengaluru 560 065, India

Abstract Garcinia gummi-gutta (L.) Roxb. (Clusiaceae) is an endemic, semidomesticated, fruit-yielding tree species distributed in the Western Ghats of India and Sri Lanka. Various bioactive phytochemicals, such as garcinol, benzophenones and xanthones are isolated from G. gummi-gutta and have shown antibacterial, antiviral and antioxidant activities. We sequenced the total genomic DNA using Illumina Hiseq 2000 platform and examined 241,141,804 bp high quality data, assembled into 773,889 contigs. In these contigs, 27,313 simple-sequence repeats (SSRs) were identified, among which mononucleotide repeats were predominant (44.98%) followed by dinucleotide and trinucleotide repeats. Primers were designed for 9964 microsatellites among which 32 randomly selected SSR primer pairs were standardized for amplification. Polymerase chain reaction (PCR) amplification of genomic DNA in 30 G. gummi-gutta genotypes revealed polymorphic information content (PIC) across all 32 loci ranging from 0.867 to 0.951, with a mean value of 0.917. The observed and expected heterozygosity ranged from 0.00 to 0.63 and 0.896 to 0.974, respectively. Alleles per locus ranged from 12 to 27. This is the first report on the development of genomic SSR markers in G. gummi-gutta using next-generation sequencing technology. The genomic SSR markers developed in this study will be useful in identification, mapping, diversity and breeding studies.

[Ravishankar K. V., Vasudeva R., Hemanth B., Sandya B. S., Sthapit B. R., Parthasarathy V. A. and Rao V. R. 2017 Isolation and characterization of microsatellite markers in Garcinia gummi-gutta by next-generation sequencing and cross-species ampliﬁcation. J. Genet. 96, 213–218]

Introduction rind of the uppage fruit has been traditionally used in India and Sri Lanka as a culinary additive and fish preserva- Garcinia gummi-gutta (L.) Roxb., popularly known as ‘Mal- tive (Samarajeewa and Shanmugapirabu 1983; Bhagyavanth abar Gamboge’ (in English) ‘uppage’ (in Kannada) belongs et al. 2010). Many studies showed that hydroxycitric acid to the family Clusiaceae. It is a moderate-sized dioecious (HCA), a secondary compound present in the rind of uppage tree with round canopy, drooping branches and smooth dark fruit is effective in weight loss (Jena et al. 2002). The barks. It is common in lower Shola forests of the West- ern Ghats, India, up to an altitude of 1800 mean sea level. extract obtained from this fruits has exhibited the property of G. gummi-gutta is recognized in Ayurveda, the traditional antiobesity. Recently, a number of studies have shown that Indian system of medicine, for better digestion and is pre- G. gummi-gutta fruit extracts are rich in HCA and are effec- scribed against abdominal disorders and heart diseases. The tive in reducing body weight (Shara et al. 2004; Saito et al. fruit rind is ground and used as a sour flavouring spice in 2005). the preparation of curries and to garnish fish preparations. The Germplasm utilization and conservation requires precise information on the genetics, genetic relationships and diver- ∗ For correspondence. E-mail: [email protected]; sity. Earlier, a few studies attempted to examine diversity [email protected]. using random amplified polymorphic DNA (RAPD) and

Keywords. microsatellite markers; cross-species ampliﬁcation; next-generation sequencing; Garcinia gummi-gutta.

Journal of Genetics, DOI 10.1007/s12041-017-0756-0, Vol. 96, No. 2, June 2017 213 K. V. Ravishankar et al. inter-simple sequence repeat (ISSR) markers (Mohan et al. PCR and genotyping 2012; Parthasarathy et al. 2013). However, development of We selected 50 SSR primer sets randomly and synthe- microsatellite markers have helped in fingerprinting unique sized with M13 tail. These M13 tailed primers were trees, assessing degree of diversity in the populations, and first screened for amplification using pooled total genomic identifying marker tightly linked to the important agronomic DNA from five randomly selected genotypes. We used traits like active ingredients, and resistance to biotic and fluorescence-based M13 tailing PCR method following abiotic stresses. Therefore, the development of markers has Schuelke (2000) to amplify the microsatellites in a quick, become a prerequisite for genetic studies (Bohra et al. 2011; accurate and efficient manner. The forward primer tailed Dutta et al. 2011). Keeping this in view, here, we report the with 5 -GTAAAACGACGGCCAGT-3 and reverse primer development and standardization of microsatellite markers tailed with 5 -GTTTCTT-3 . PCR was carried out in 20 μL of G. gummi-gutta using next-generation sequencing (NGS) reaction volume containing 2 μLof10× reaction buffer, and their cross species transferability. 2.0 μL of 1 mM dNTPs, 0.9 μL (5 pmol) of forward, 0.9 μL reverse primers (5 pmol), labelled M13 probes (HEX, NED, Materials and methods VIC, TET) 1.2 μL(5pmol),5.0μL (50–75 ng) of tem- plate genomic DNA, 0.8 μL(2U)ofTaq DNA polymerase Plant materials and 7.2 μL of nuclease free water. The PCR cycling profile The total genomic DNA from G. gummi-gutta was used for was: initial denaturation at 94◦C for 2 min, followed by 35 genome sequencing. We have also included Garcinia indica cycles of 94◦C for 30 s, 55◦C for 30 s, 72◦Cfor1minanda and Garcinia morella to examine cross species transferabil- final extension at 72◦C for 5 min. PCR reaction was carried ity of isolated microsatellite markers. The leaf material was out using thermocycler (Eppendrof Master Cycler Gradient, obtained from the germplasm collection of the College of Germany). Amplified products were initially separated on Forestry, Sirsi (University of Agricultural Sciences, Dhar- 3% agarose gel to confirm the amplification. Finally, 32 SSR wad), India (see details in table 1 in electronic supplementary primers were selected based on amplification of clear PCR material at http://www.ias.ac.in/jgenet/). The leaf samples products. These primers were employed for amplification of ◦ were obtained from Jaddi Gadde collection (14 44 13.2 N 30 genotypes of G. gummi-gutta and one genotype each of ◦ latitude and 74 43 03.2 E longitude with altitude of 498 m). G. indica and G. morella. The PCR products were separated The authenticated herbarium specimens were deposited to using automated DNA Sequencer (Applied Biosystems, ABI the herbarium of College of Forestry, Department of Forest 3730 DNA Analyzer) through capillary electrophoresis, at Biology and Tree improvement. M/S Eurofins facility, Bengaluru.

Genome sequencing and assembly Genetic analysis of SSR markers High-quality genomic DNA was isolated from the leaves of 30 G. gummi-gutta genotypes, one genotype from each of G. The raw data generated was analysed and compiled using indica and G. morella (table 1 in electronic supplementary Peak Scanner ver. 1.0 software (Applied Biosystems, Foster material) following modiﬁed CTAB method (Ravishankar Table 1. Details of sequenced genomic data. et al. 2000). Total genomic DNA was sequenced using NGS Illumina HiSeq2000 platform at M/s Genotypic Bengaluru Sequencing details facility following manufactures instructions. High-quality sequence reads (Q > 20; >70% bases in a read) were used Total number of contigs 773889 for de novo assembly into contigs using SOAPdenovo2 soft- Total number of examined sequences (bp) 241141804 Total number of identiﬁed SSRs 27313 ware (Luo et al. 2012). Assembly with Kmer-63 was selected Number of SSR containing contigs 26663 as it has the optimal readings for N50. Number of SSR containing more than one SSR 631

Survey, identification and design primers for genomic Table 2. Simple-sequence repeat types in the G. gummi-gutta microsatellite markers contigs sequences. The Perl Script software program MISA (http://pgrc.ipk- gatersleben.de/misa/)(Thielet al. 2003; Ravishankar et al. 2015) Motif length Number of SSR Frequency (%) was used for identification of SSRs from assembled con- Mononucleotide 12286 44.98 tigs (Feng et al. 2009; Ravishankar et al. 2015). MISA Dinucleotide 9638 35.29 files were transferred to Microsoft Excel where SSRs were Trinucleotide 3003 10.99 classified into mononucleotide, dinucleotide, trinucleotide, Tetranucleotide 552 2.02 tetranucleotide, pentanucleotide and hexanucleotide repeats Pentanucleotide 249 0.91 and compound repeats. Primer pairs flanking the repeats Hexanucleotide 58 0.21 Complex/compound 1527 5.59 were designed using Primer3 software (Untergrasser et al. Total 27313 2012)(table2 in electronic supplementary material).

214 Journal of Genetics, Vol. 96, No. 2, June 2017 Microsatellite markers in Garcinia gummi-gutta ampliﬁcation Cross species of identity ) content (PI) GI GM e H )( o Number Observed Observed Expected Polymorphic Probability type (k) (bp) ( H Repeat of allele size range heterozygosity heterozygosity information 3 → G. gummi-gutta. Reverse sequence 5 3 → CCACC AATGGG CAGGT TCGACG GGAACGGGCA AACACGA GGTGT AGCGAAGGCA GATCT AGCCCA GGGGA CACCT ACCACt GAGGG GCTCAT TCTTGC CTTGGC CCCGT GATGCC AACCA CCGACT GTAGCC AATGGC GTTGC ACCCCA CCAA AAGCCA GTGCGG ATACCTT CATGT TTTGGGT AGGCT GCTTGGT CCCCTN GGCATGT TGCTC TGGATGTA CCACCT TTCCCCAA TATGATGAGC CAAGCCAT ACTACAGA ACAAATCCA CCAACTCG Genetic analysis of microsatellite markers developed for Locus nameGG_KVRf282 Forward sequence TTTTGCACAAGCACACG 5 GG_KVRf283 TTTTCTCAATCTCCATGCA TGTAGTCCTCCTTCAGGTGG_KVRf293 AAAGGCCAAGAGCCTAA (AG)6 TTTGCCAGGGGAAAAC (AC)6 24 24 TGGGTTTTGGTTTATGCC 107–163 (AG)7 101–255 0.296 19 0.375 100–191 0.957 0.05 0.925 0.936 0.900 0.965 0.0065 0.0169 NA 0.938 NA NA NA 0.0252 NA NA GG_KVRf294 TTTCTGCAGCATGGCCGG_KVRf619 TTGAACTAGGGAGGGCA TGTATATTTTGTGTGGCAGGG_KVRf659 (TG)7 TGGACTAGGGTGAGCC TGGTTGGCTTGCATATGT 19 CATTCACATTCATGTTGGCCA (AT)11 (AC)12 100–186 23 25 199–284 0.000 132–210 0.087 0.222 0.969 0.974 0.914 0.942 0.951 0.893 0.0243 NA 0.0105 0.0133 NA NA NA NA NA GG_KVRf553 TGTGAACATGCATGCCTT TCGGGTTGTTCCTCACAT (TG)8GG_KVRf716 24 TGGTATGGATGGCATATGG GGGGGTGAGCAAACAACGG_KVRf717 330–460 TGGTATGGATGGCATATG (AT)6GG_KVRf796 0.208 TGGGCTATACATGGTGACA GGGGTATCACTATAGC 27 GCCATGTGGATCCGCTTAGGG_KVRg194 (AT)6 TGCACCAGACAGTCGATT 126–278 (AT)8 0.969 TGGGGGTTTGATCCATGG 22 23 0.36 (CA)7 192–286 160–264 0.946 20 0.208 0.292 0.965 143–241 0.0089 0.364 A 0.964 0.951 0.943 A 0.964 0.941 0.0075 0.928 NA 0.0098 0.939 0.0120 NA NA NA 0.0157 NA A NA A GG_KVRg313 TGAGCATGCCATCTATTTGT GGACGGTCACTGGAAAGGG_KVRj151 AACAACGAGGGCGTCGT (AT)6GG_KVRj152 AAATGGTGGCAAGACACAC 17 CATATCACCATCACCACCGG_KVRi741 TGCCACCTTCCAGGCAC (TG)6 302–451 ACCACTGTTCCAACCAT (AC)6GG_KVRi010 18 AGAAGGTGGTGGAGGTGTA 0.143 15 ACCACTCTGTCACGAC (CT)6GG_KVRi011 119–215 146–243 AGGGGGAGGGGGTTTT 20 TGGTTGTGTAGGGATGTG 0.308 0.954 (TG)6 391–498 0.091 GTGTGTGCGTGTGTGG 15 0.115 0.927 0.918 (CA)6 349–394 0.943 17 0.0224 0.949 0.893 0.2 241–351 0.916 NA 0.0151 NA 0.37 0.927 0.0204 0.936 NA A 0.0089 A 0.943 0.906 NA NA NA 0.921 0.0324 NA 0.0093 A NA NA GG_KVRg565 TCCTATTACCCGCCCCGG_KVRg470 TCGGGTGTCAGAACCGT GGAGGGCGGGATATATGTTG (AT)10 GGTGTGGTATGCAGCTCTGT (TA)6 18 201–247 17 106–188 0.519 0.37 0.937 0.945 0.914 0.923 0.0103 NA 0.0089 A A NA GG_KVRg670 TCCACTCACAAGCCAACGG_KVRg756 TCACGAAGAGAACCACTT ACCCAAAAGAACCGGTGG_KVRj174 TCCTCGCCTCGTAACCTC TTTGTCTGAGGCCTGTG (TA)7 (GA)11GG_KVRj198 16 12 TTGCCCCCAACCCAGA GCAATGGAGGCAATGCT 145–232 (TTG)6 185–285 TGTTGCACTTGTGCCTGTT 19 (CAA)9 0.4 0.000 18 143–305 102–158 0.321 0.913 0.942 0.077 0.939 0.88 0.918 0.937 0.0384 0.917 0.0120 0.913 A A 0.0097 A 0.0116 A NA A A A Table 3.

Journal of Genetics, Vol. 96, No. 2, June 2017 215 K. V. Ravishankar et al.

City, USA) for determining the exact allele size. Allele sizes for each SSR loci were used for genetic analysis using Cervus 3.0 software (Kalinowski et al. 2007). We have esti- mated the number of alleles, observed heterozygosity (Ho), ampliﬁcation Cross species expected heterozygosity (He) and PIC. Probability of identity (PI) was analysed for each SSR loci using Identity 1.0 software (Wagner and Sefc 1999). of identity Results and discussion Off the wide range of DNA markers in use, microsatellites or SSR markers are extensively employed in plant studies. SSR markers are highly reproducible, multiallelic, PCR based, highly polymorphic, easy to use and amenable to automa-

) content (PI) GI GM tion. Thus, SSRs markers are widely used for mapping, crop e

H breeding programmes and population genetics (Varshney et al. 2005). However, the use of microsatellite markers for study- ing nonmodel species like G. gummi-gutta has been impeded by lack of available genomic resources. A few years ago, )(

o identiﬁcation of genomic SSRs and subsequent conversion to markers were expensive and time-consuming, involving con- struction and screening of microsatellite-enriched genomic DNA libraries (Glenn and Schable 2005). Compared to this hybrid capture method using probes, the present NGS based method is fast, simple, and overcomes a number of technical difﬁculties. The advent of NGS technologies, such as pyrose- quencing, has made this process less complicated and easy (Zalapa et al. 2012). As a result, a large number of SSR mark- Number Observed Observed Expected Polymorphic Probability ers can be developed in a short span of time and at a lower cost. This approach is especially useful for many tree crops where there is no sequence information available. type (k) (bp) ( H Repeat of allele size range heterozygosity heterozygosity information We used high-throughput IlliminaHiSeq 2000 platform to develop genomic SSR markers in G. gummi-gutta.The

3 number of assembly of reads of the long sequences was

→ 773,889 contigs of total length 241 Mb (table 1). An SSR survey of genomic sequences using MISA software (http:// pgrc.ipk-gatersleban.de/misa) revealed that the 773,889 contigs contained 27,313 SSRs. Mononucleotide repeats are predominant (44.98%) followed by dinucleotide (35.29%) and trinucleotide (14.9%) repeats (table 2). Mononucleotide repeats are present in high number in some monocots Reverse sequence 5 (rice, sorghum and Brachypodium) and also in some dicots (Arabidopsis, Medicago and Populus) (Sonah et al. 2011). ; A, ampliﬁed; NA, not ampliﬁed. 3 In the present study, apart from mononucleotide repeats, din-

→ ucleotide repeats were the most prevalent accounting for 35.29% of all SSRs identiﬁed, followed by trinucleotide repeats (10.29%; table 2). While the mononucleotide, dinucleotide and trinucleotide repeats contribute to the major pro- CACCA GCAA GGCCA GGGTTT GCTATT CTTTT ATGCTT GAAA GTGCCTTGCTCA AGCCT CATGAA GGGGGT GGCAT AAACGAportion CAGCCC of SSRs (90.56%) and the rest was contributed by GAGGGGT ATCCT tetranucleotide, pentanucleotide and hexanucleotide repeats

Garcinia morella ;GM, (table 2). The molecular mechanism of the origin and evolution of microsatellite markers are not clearly understood. The ) relative dominant occurrence of repeat motif of a particular sequence types and its length in plant genome might be the ( contd outcome of selection pressure applied on that speciﬁc motif

Garcinia indica during evolution. The most common mutation mechanisms GG_KVRj447 TGCTGAGATGGCAGCG ACTGGTCGCCCTGAGGTG (CCA)5 18 110–211GG_KVRk769 0.179 ATGCCGCACACCATGCGG_KVRk831 TGTATTTCGGTCCATTAGC GGGGAGGGTGTGCGTTG CCATGAGCCCAAGTGC (AATG)5 0.932 (TTTTC)5 18 24 309–411 0.91 237–324 0.208 0.63 0.0114 A 0.941 0.953 NA 0.916 0.932 0.0144 0.0070 NA NA NA NA GG_KVRj345 TGGGGCATGCTCTGATC GTGGGGGTGGCAAATGAG (TGG)6GG_KVRj541 TGAGGCATAAAATGCAC 12GG_KVRj603 TGGACCTAGCCATGCCC TCTCATGCGAAAGCTCAT 105–178 (TCA)5 TTGCCACCTAAGCATGCGG_KVRj938 (TCA)5 GCAACATCCCAAGTGT 0.115 20GG_KVRk715 TCCCCATCACCTTCCAC TGGTGCCATCCAGTTAG 14 226–337 (ACC)5 AGGGTGAGTTTTTGGC 0.916 227–302 0.259 (AATA)6 17 0.208 13 128–197 0.89 0.945 175–268 0.233 0.896 0.0165 0.118 0.923 NA 0.919 0.867 A 0.923 0.0089 0.0245 A 0.897 0.888 A NA 0.0163 NA 0.0622 A NA A NA Locus nameGG_KVRj225 Forward sequence 5 TGTTGGATCCGTTGTT TGGGGAGAGTGGCATTG (TTG)6 18 103–192 0.593 0.943 0.92GI, 0.0093 A NA

Table 3 assumed to be operating are replication slippage, and unequal

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Table 4. Summary of genetic analysis. References

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Received 10 December 2015, in ﬁnal revised form 8 July 2016; accepted 11 July 2016 Unedited version published online: 13 July 2016 Final version published online: 17 April 2017

Corresponding editor: RAJIVA RAMAN

218 Journal of Genetics, Vol. 96, No. 2, June 2017